2023
Moresco, Marlon, Marcelo Brutti Righi, and Eduardo Horta. “Minkowski deviation measures.” Statistics & Risk Modeling 40.1-2 (2023): 1-19.
Bayer, F. M.; Pumi, G.; Pereira, T. L. and Souza, T. C. (2023) Inflated beta autoregressive moving average models. Computational And Applied Mathematics (Forthcoming).
Benaduce, H. S. e Pumi, G. (2023). SYMARFIMA: a dynamical model for conditionally symmetric time series with long range dependence mean structure. Journal of Statistical Planning and Inference v. 225, p. 71–88.
Chen, Y., Torrent H. S., and Ziegelmann F. A. (forthcoming, 2023+). Robust Nonparametric Frontier Estimation in Two Steps, Econometric Reviews.
Pumi, G.; Prass, T. S. and Lopes, S. R .C. (2023). A novel copula-based approach for parametric estimation of univariate time series through its covariance decay. Statistical Papers (Forthcoming)
M. Sagrillo, R. R. Guerra, F. M. Bayer and R. Machado, “The LA Distribution: An Approximation of the G0A Distribution for Amplitude SAR Image Modeling,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-8, 2023, Art no. 5205408, doi: 10.1109/TGRS.2023.3262480.
Taufemback, C.G. (2023), Asymptotic Behavior of Temporal Aggregation in Mixed-Frequency Datasets. Oxford Bulletin of Economics and Statistics, forthcoming, https://doi.org/10.1111/obes.12546
Taufemback, C.G. (2023), Non-parametric short- and long-run Granger causality testing in the frequency domain. Journal of Time Series Analysis, 44: 69-92. https://doi.org/10.1111/jtsa.12650
2022
de Oliveira, B.N.; Valk, M; Marcondes Filho, D. 2022. Fault detection and diagnosis of batch process dynamics using ARMA-based control charts. Journal of Process Control. 111:46-58.
GRANDE, A.F. ; PUMI, G. ; CYBIS, G. B. . Granger causality and time series regression for modeling the migratory dynamics of Influenza into Brazil. SORT-Statistics and Operations Research Transactions, v. 46, p. 161-188, 2022.
PALM, B. G.; BAYER, F. M.; CINTRA, R. J.. 2-D Rayleigh autoregressive moving average model for SAR image modeling. Computational Statistics & Data Analysis, v. 171, p. 107453, 2022.
Taufemback, Cleiton G., Troster, Victor and Shahbaz, Muhammad. “A Robust Test for Monotonicity in Asset Returns” Journal of Time Series Econometrics, vol. 14, no. 1, 2022, pp. 1-24. https://doi.org/10.1515/jtse-2019-0068
2021
Fernandes, Marcelo, Emmanuel Guerre, and Eduardo Horta. “Smoothing quantile regressions.” Journal of Business & Economic Statistics 39.1 (2021): 338-357.
GUERRA, RENATA ROJAS; PEÑA-RAMÍREZ, FERNANDO A. ; BOURGUIGNON, MARCELO. The unit extended Weibull families of distributions and its applications. JOURNAL OF APPLIED STATISTICS, v. 48, p. 3174-3192, 2021.DOI: https://doi.org/10.1080/02664763.2020.1796936
Prass, T. S. and Pumi, G. (2021) On the behavior of the DFA and DCCA in trend-stationary processes. Journal of Multivariate Analysis v. 182, p. 104703.
Pumi. G.; Prass, T. S. and Souza, R. R. (2021) . A dynamic model for double-bounded time series with chaotic-driven conditional averages. Scandinavian Journal of Statistics, v. 48, p. 68-86.
RIBEIRO, T. F. ; CORDEIRO, G. M. ; PEÑA-RAMÍREZ, F. A. ; GUERRA, R. R. . A new quantile regression for the COVID-19 mortality rates in the United States. COMPUTATIONAL AND APPLIED MATHEMATICS, v. 40, p. 255, 2021. DOI: https://doi.org/10.1007/s40314-021-01553-z
Scher, V. T.; Cribari-Neto, F. ; Pumi, G. and Bayer, F. M. (2021). Goodness-of-fit tests for βARMA hydrological time series modeling. Environmetrics, v. 31, p. env.2607.
Valk, M; Cybis GB. 2021. U-statistical inference for hierarchical clustering. Journal of Computational and Graphical Statistics. 30(1)
2020
Marcondes, DF; Valk M. 2020. Dynamic Var Model-Based Control Charts for Batch Process Monitoring. European Journal of Operational Research (EJOR). 285(1):296-305.
MELCHIOR, CRISTIANE ; ZANINI, ROSELAINE RUVIARO ; GUERRA, RENATA ROJAS ;
ROCKENBACH, DINEI A. . Forecasting Brazilian mortality rates due to occupational accidents using autoregressive moving average approaches. INTERNATIONAL JOURNAL OF FORECASTING, v. 37, p. 825-837, 2020. DOI:https://doi.org/10.1016/j.ijforecast.2020.09.010
Pumi, G.; Rauber, C. and Bayer, F. M.(2020). Kumaraswamy regression model with Aranda-Ordaz link function. TEST, v. 29, p. 1051-1071.
2019
Pumi, G.; Valk, M.; Bisognin, C.; Bayer, F. M. and Prass, T. S. (2019). Beta autoregressive fractionally integrated moving average models. Journal of Statistical Planning and Inference, v. 200, 196–212.
2018
Horta, Eduardo, and Flavio Ziegelmann. “Dynamics of financial returns densities: A functional approach applied to the Bovespa intraday index.” International Journal of Forecasting 34.1 (2018): 75-88.
CYBIS, GABRIELA B.; VALK, MARCIO ; LOPES, SÍLVIA R. C. . Clustering and classification problems in genetics through U -statistics. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v. 88, p. 1882-1902, 2018.
Horta, E., and Ziegelmann F. A. (2018). Conjugate processes: Theory and application to risk forecasting, Stochastic Processes and their Applications, Volume 128, p.727-755.
PEÑA-RAMÍREZ, F. A. ; GUERRA, R. R. ; CORDEIRO, G. M. ; DINIZ, P. R. M. . The Exponentiated Power Generalized Weibull: Properties and Applications. ANAIS DA ACADEMIA BRASILEIRA DE CIÊNCIAS (ONLINE), v. 90, p. 2553-2577, 2018. DOI: https://doi.org/10.1590/0001-3765201820170423
2017
BAYER, F. M.; BAYER, D. M.; PUMI, G.. Kumaraswamy autoregressive moving average models for double bounded environmental data. Journal of Hydrology, v. 555, p. 385-396, 2017.
COUTINHO, VITOR A.; CINTRA, R. J.; BAYER, F. M.. Low-complexity Multidimensional DCT Approximations for High-order Tensor Data Decorrelation. IEEE Transactions on Image Processing, v. 26, p. 1-1, 2017.
CYBIS, G. B.; SINSHEIMER, J. S. ; BEDFORD, TREVOR ; LEMEY, P. ; SUCHARD, A. M. . Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza. STATISTICS IN MEDICINE, v. 1097, p. sim.7196, 2017.
2016
Horta, Eduardo, and Flavio Ziegelmann. “Identifying the spectral representation of Hilbertian time series.” Statistics & Probability Letters 118 (2016): 45-49.
PRASS, T. S.; LOPES, SÍLVIA R.C. ; ACHCAR, J. A. . MCMC Bayesian Estimation in FIEGARCH Models. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v. 45, p. 3238-3258, 2016.
2015
BAYER, F. M.; CRIBARI-NETO, F.. Bootstrap-based model selection criteria for beta regressions. Test, v. 24, p. 776-795, 2015.
CYBIS, GABRIELA B.; SINSHEIMER, JANET S. ; BEDFORD, TREVOR ; MATHER, ALISON E. ; LEMEY, PHILIPPE ; SUCHARD, MARC A. . Assessing phenotypic correlation through the multivariate phylogenetic latent liability model. The Annals of Applied Statistics, v. 9, p. 969-991, 2015.
2014
LOPES, SÍLVIA R.C. ; PRASS, TAIANE S. . Theoretical results on fractionally integrated exponential generalized autoregressive conditional heteroskedastic processes. Physica. A (Print), v. 401, p. 278-307, 2014.
2013
CYBIS, G. B.; SINSHEIMER, J. S. ; LEMEY, P. ; SUCHARD, M. A. . Graph hierarchies for phylogeography. Philosophical Transactions – Royal Society. Biological Sciences (Print), v. 368, p. 20120206-20120206, 2013.
LOPES, SÍLVIA R.C. ; PRASS, TAIANE S. . Seasonal FIEGARCH processes. Computational Statistics & Data Analysis (Print), v. 68, p. 262-295, 2013.
2012
Valk, M; Pinheiro AS. 2012. Time-series clustering via quasi U-statistics. Journal of Time Series Analysis. 33:608-619.
Filho, Silva O. C., Ziegelmann F. A., and Dueker M. (2012). Modeling dependence dynamics through copulas with regime switching, Insurance Mathematics & Economics, Volume 50, p.346-356.
2011
CINTRA, R. J.; BAYER, F. M.. A DCT Approximation for Image Compression. IEEE Signal Processing Letters, v. 18, p. 579-582, 2011.
2010
Bathia, N., Yao Q., and Ziegelmann F. A. (2010). Identifying the Finite Dimensionality of Curve Time Series, Annals of Statistics, Volume 38, p.3352-3386.
2002
Ziegelmann, Flavio A. (2002). The Local Exponential Estimator, Econometric Theory, Volume 18, Issue 4, p.985-992, (2002)